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Google Cloud is announcing a new offering meant to accelerate digital transformation in the manufacturing sector, an industry sector that has often been a laggard when it comes to modernization and moving to the cloud.

The new offering runs on the Google Cloud Platform and consists of the Manufacturing Data Engine and Manufacturing Connect – two solutions that are targeted at connecting siloed data assets, rationalizing the data into a standard format, and enabling advanced analytics using the Google tool set, including its AI capabilities.

Manufacturing Data Engine integrates a variety of Google Cloud components like Dataflow, PubSub, BigQuery, Cloud Storage, Looker, Vertex AI, Apigee, etc., into a manufacturing-specific solution. Additionally, Manufacturing Connect is a factory edge platform codeveloped with Litmus Automation that connects to and streams data from manufacturing assets to Google Cloud based on a library of more than 250 machine protocols.

The primary targeted analysis areas for this new technology suite are three high-value use cases, including; manufacturing analytics and insights, predictive maintenance and machine-level anomaly detection. All are targeted at keeping the production line running.

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Breaking down the silos

Manufacturing is one of the most siloed verticals there is when it comes to data assets and analysis. Many years of disparate systems running various aspects of the business, often from a multitude of vendors, have led to non-compatible data and stand-alone processing. Indeed, operations like raw materials acquisition, machine operations, quality control, shipping, etc. are often stand-alone or minimally connected environments.

With so many systems and such disparity in information and data access, it’s difficult to analyze the “big picture” across the entire ecosystem of manufacturing operations. Implementing a system that can pull the various data from different systems and combine it meaningfully and prepare it for analysis is a great opportunity to enhance the overall manufacturing process. Indeed, analytics using AI holds the potential for seeing the “big picture” where individual analysis of overall process components may not.

But updating is not easy. It’s very hard to modernize a working manufacturing operation, as any disruption to the process flows required to bring on new systems can be extremely costly. It’s why many companies continue to use equipment that may have been installed years or even decades ago. If it works, don’t mess with the process — that is the mindset in those instances. As a result, manufacturing modernization is a long-term process that in most companies will take years to achieve.

Ford requires expanded analytics

With the growth of sensors placed strategically across the many machines and processes involved in manufacturing, there is a growing need to analyze all of that data in a holistic fashion. Ford worked with Google to implement a platform operating on more than 100 machines connected across two plants, streaming and storing over 25 million records per week, helping Ford to implement predictive and preventive actions in their plants.

As Ford moves to a modernized environment, and in particular during its transition to a new business as it phases out gas-powered vehicles and moves to EVs, it is imperative that they move their data assets as well. While Ford has certainly updated some of its systems, a complete digital transformation of the manufacturing operations is an ongoing process. But with the march toward a new production model for its new vehicles, this is a prime opportunity for Ford to make creative and substantial changes.

But any process changes affect not only internal Ford operations but also all of Ford’s suppliers, as data visibility (and feedback) can change the way they work with Ford. Expect the company to expand this umbrella to include remote access to data from its suppliers in the future.

Bottom line:

The need for cloud-based compute has been hampered by the hard-to-move data silos across many systems, mostly internal but increasingly being fed from outside the company as well. Running new capabilities like AI/ML in a large data center, cloud based or otherwise, can’t take place if you can’t get to the data effectively. Therefore, being able to bring in data from disparate places, harmonize the data so it’s compatible, and then utilize it to run modern data insights with tools not previously available is critical to the productivity of the enterprise and key to enabling Manufacturing’s move into the next generation.

While the other hyperscalers (e.g., AWS, Microsoft Azure) have manufacturing-centric initiatives in place as well, Ford’s partnership with Google indicates that it believes Google provides their company with an optimized cloud-based computing environment, at least in the three key stated areas that this effort is focused on. The working partnership is an endorsement of the AI and analytics capabilities that Google provides.

I don’t expect Ford to be exclusively reliant on Google Cloud going forward with its many diverse business units. It may even deploy other cloud-based systems in its manufacturing operations. However, this move in its manufacturing business is a major undertaking that is clearly mission-critical to its long-term success.

I further expect that both Ford and Google will learn much from this partnership that Google can apply to its other cloud services customers. Additionally, I predict that Google may also extend more analytics and business process automation features for Ford and customers in other markets. It also offers a major win for itself as a cloud provider that is number three in the marketplace (behind AWS as #1 and Azure as #2), but that continues to gain momentum with its new emphasis on winning enterprise customers.

Jack Gold is the founder and principal analyst at J.Gold Associates.


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